Abstract: In some examples, a server instructs individual software applications to process individual tasks and determines a plurality of outputs resulting from processing. The server determines, based on the plurality of outputs, individual performance scores associated with individual software applications, and determines individual features associated with individual task data of multiple task data. The server receives task data associated with a task, determines at least one feature associated with the task data based on analyzing the task data and predicts, using at least one machine learning model, the individual performance scores associated with the individual software applications that have processed the task. The server selects at least one software application from the plurality of software applications based on an associated performance score, generates a recommendation of the at least one software application, and transmits the recommendation to the user device.

Abstract: A method for facilitating performing of tasks optimally using software applications. The method includes receiving, using a communication device, task data from a user device, analyzing, using a processing device, the task data, extracting, using the processing device, a feature associated with the task data, analyzing, using the processing device, the feature using a machine learning model, training the machine learning model for predicting a performance score associated with each of software applications capable of performing a task, selecting, using the processing device, a software application from the software applications based on the performance score, generating, using the processing device, a recommendation of the software application based on the selecting, and transmitting, using the communication device, the recommendation and the output of the chosen software application based on the task input data, to the user device.

Abstract: A server receives a request including information associated with an objective and generates, using a first machine learning model, an architecture of an artificial intelligence-based solution to address the objective. The server generates the artificial intelligence-based solution based on the architecture by: identifying a second machine learning model in a first database and identifying a third machine learning model in a second database. The second machine learning model is a first portion of the artificial intelligence-based solution and is available via a marketplace. The third machine learning model is a second portion of the artificial intelligence-based solution and is unavailable via the marketplace. The server generates the artificial intelligence-based solution based at least in part on a combination of: the second machine learning model and the third machine learning model. The server enables access to the artificial intelligence-based solution via the marketplace.

Abstract: A method for facilitating performing of tasks optimally using software applications. The method includes receiving, using a communication device, task data from a user device, analyzing, using a processing device, the task data, extracting, using the processing device, a feature associated with the task data, analyzing, using the processing device, the feature using a machine learning model, training the machine learning model for predicting a performance score associated with each of software applications capable of performing a task, selecting, using the processing device, a software application from the software applications based on the performance score, generating, using the processing device, a recommendation of the software application based on the selecting, and transmitting, using the communication device, the recommendation and the output of the chosen software application based on the task input data, to the user device.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method further includes receiving information associated with a requested operator, and generating, by a processing device executing a first machine learning model, a skeleton architecture of an AI-based solution to the operator based on the information.

Abstract: A method receiving a dataset, storing the dataset in a secured drive, synthesizing a representative dataset, in the secured drive, based on the dataset, granting access to a specialist to view the representative dataset, receiving a model that was generated using the representative dataset, running the model on the representative dataset, validating the results of running the model on the representative dataset, and presenting the validated results of running the model on the representative dataset in a graphical user interface.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method also includes receiving information associated with a requested operator and identifying, by a processing device executing a first machine learning model, a second machine learning model corresponding to an AI-based solution to the operator.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method further includes receiving information associated with a requested operator, and generating, by a processing device executing a first machine learning model, a skeleton architecture of an AI-based solution to the operator based on the information.

Abstract: A method includes using a generator to generate a first result, providing the first result to a critic, generating a first surprise factor based on providing the first result to the critic, based on the first surprise factor, using the generator to generate a second result, providing the second result to the critic, generating a second surprise factor based on providing the second result to the critic, based on the second surprise factor, determining that the generator has generated a most surprising result, and presenting the most surprising result in a graphical user interface.